imodels
intro-to-python
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| imodels | intro-to-python | |
|---|---|---|
| 7 | 1 | |
| 1,290 | 871 | |
| - | - | |
| 8.5 | 0.0 | |
| 5 days ago | over 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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imodels
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[D] Have researchers given up on traditional machine learning methods?
- all domains requiring high interpretability absolutely ignore deep learning at all, and put all their research into traditional ML; see e.g. counterfactual examples, important interpretability methods in finance, or rule-based learning, important in medical or law applications
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What would be my best approach given the data I have?
Next, this variable will be your target and you can use various supervised learning models to answer your question. Since interpretation is key, you can use something from here: https://github.com/csinva/imodels or do some black box models and use shab to understand which features contributed most.
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Random Forest Estimation Question
Option 2) fit a model from https://github.com/csinva/imodels on the predicted values of the RF
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UC Berkeley Researchers Introduce ‘imodels: A Python Package For Fitting Interpretable Machine Learning Models
Despite recent breakthroughs in the formulation and fitting of interpretable models, implementations are frequently challenging to locate, utilize, and compare. imodels solves this void by offering a single interface and implementation for a wide range of state-of-the-art interpretable modeling techniques, especially rule-based methods. imodels is basically a Python tool for predictive modeling that is simple, transparent, and accurate. It gives users a straightforward way to fit and use state-of-the-art interpretable models, all of which are compatible with scikit-learn (Pedregosa et al., 2011). These models can frequently replace black-box models while boosting interpretability and computing efficiency without compromising forecast accuracy. Continue Reading
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[D] Looking for open source projects to contribute
Our package imodels is expanding our sklearn-compatible set of interpretable models and always looking for new contributors!
- imodels: a package extending sklearn with state-of-the-art models for interpretable data science (e.g. Bayesian Rule Lists, RuleFit)
- imodels: a package extending sklearn with state-of-the-art interpretable models (e.g. Bayesian Rule Lists, RuleFit) from BAIR [P]
intro-to-python
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Show HN: Intro to Python and Programming for non-CS majors (revisited)
Hi there,
I am the author of this Show HN post: https://news.ycombinator.com/item?id=22669084
Back then, I released the materials for my Intro to Python course "to the world". GitHub repo: https://github.com/webartifex/intro-to-python
I incorporated many of the constructive criticism and am currently recording a video lecture series on YouTube: https://www.youtube.com/playlist?list=PL-2JV1G3J10kRUPgP7EwLhyeN5lOZW2kH
I guess that a lot of people without a CS background would find these resources valuable and am open for further feedback.
If you have any "non-tech" friends who want to learn to code, please feel free to direct them to my course.
Stay healthy everybody!
What are some alternatives?
pycaret - An open-source, low-code machine learning library in Python
ipp - An intro to Python & programming for wanna-be data scientists [Moved to: https://github.com/webartifex/intro-to-python]
interpret - Fit interpretable models. Explain blackbox machine learning.
python-training - Python training for business analysts and traders
shap - A game theoretic approach to explain the output of any machine learning model.
z3_tutorial - Jupyter notebooks for tutorial on the Z3 SMT solver
linear-tree - A python library to build Model Trees with Linear Models at the leaves.
feature-engineering-tutorials - Data Science Feature Engineering and Selection Tutorials
docarray - Represent, send, store and search multimodal data
JustEnoughScalaForSpark - A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
examples - Datapane Examples